We use Sockeye [1]} to train Transformer-based [2]} conditional generation models, one for causes, one for effects. Sockeye supports decoding via N-best (each step greedily chooses the top best N words in beam search based on the generated tokens) and random sampling (each step randomly sampling N words from the softmax distribution based on the generated tokens). The training data (CausalBank) is processed through Byte Pair Encoding [3]} to reduce vocabulary size.
